///*
// * Licensed to the Apache Software Foundation (ASF) under one or more
// * contributor license agreements.  See the NOTICE file distributed with
// * this work for additional information regarding copyright ownership.
// * The ASF licenses this file to You under the Apache License, Version 2.0
// * (the "License"); you may not use this file except in compliance with
// * the License.  You may obtain a copy of the License at
// *
// *    http://www.apache.org/licenses/LICENSE-2.0
// *
// * Unless required by applicable law or agreed to in writing, software
// * distributed under the License is distributed on an "AS IS" BASIS,
// * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// * See the License for the specific language governing permissions and
// * limitations under the License.
// */
//
//package com.boonya.spark.examples.ml;
//
//import org.apache.spark.sql.Dataset;
//import org.apache.spark.sql.SparkSession;
//
//// $example on$
//import java.util.Arrays;
//import java.util.List;
//
//import org.apache.spark.ml.feature.UnivariateFeatureSelector;
//import org.apache.spark.ml.linalg.VectorUDT;
//import org.apache.spark.ml.linalg.Vectors;
//import org.apache.spark.sql.Row;
//import org.apache.spark.sql.RowFactory;
//import org.apache.spark.sql.types.*;
//// $example off$
//
///**
// * An example for UnivariateFeatureSelector.
// * Run with
// * <pre>
// * bin/run-example ml.JavaUnivariateFeatureSelectorExample
// * </pre>
// */
//public class JavaUnivariateFeatureSelectorExample {
//  public static void main(String[] args) {
//    SparkSession spark = SparkSession
//      .builder()
//      .appName("JavaUnivariateFeatureSelectorExample")
//      .getOrCreate();
//
//    // $example on$
//    List<Row> data = Arrays.asList(
//      RowFactory.create(1, Vectors.dense(1.7, 4.4, 7.6, 5.8, 9.6, 2.3), 3.0),
//      RowFactory.create(2, Vectors.dense(8.8, 7.3, 5.7, 7.3, 2.2, 4.1), 2.0),
//      RowFactory.create(3, Vectors.dense(1.2, 9.5, 2.5, 3.1, 8.7, 2.5), 3.0),
//      RowFactory.create(4, Vectors.dense(3.7, 9.2, 6.1, 4.1, 7.5, 3.8), 2.0),
//      RowFactory.create(5, Vectors.dense(8.9, 5.2, 7.8, 8.3, 5.2, 3.0), 4.0),
//      RowFactory.create(6, Vectors.dense(7.9, 8.5, 9.2, 4.0, 9.4, 2.1), 4.0)
//    );
//    StructType schema = new StructType(new StructField[]{
//      new StructField("id", DataTypes.IntegerType, false, Metadata.empty()),
//      new StructField("features", new VectorUDT(), false, Metadata.empty()),
//      new StructField("label", DataTypes.DoubleType, false, Metadata.empty())
//    });
//
//    Dataset<Row> df = spark.createDataFrame(data, schema);
//
//    UnivariateFeatureSelector selector = new UnivariateFeatureSelector()
//      .setFeatureType("continuous")
//      .setLabelType("categorical")
//      .setSelectionMode("numTopFeatures")
//      .setSelectionThreshold(1)
//      .setFeaturesCol("features")
//      .setLabelCol("label")
//      .setOutputCol("selectedFeatures");
//
//    Dataset<Row> result = selector.fit(df).transform(df);
//
//    System.out.println("UnivariateFeatureSelector output with top "
//        + selector.getSelectionThreshold() + " features selected using f_classif");
//    result.show();
//
//    // $example off$
//    spark.stop();
//  }
//}
